Field of the Invention
The invention concerns a method for evaluating medical image data, and an evaluation computer, a medical imaging apparatus, and a non-transitory, computer-readable data storage medium for implementing such a method.
Description of the Prior Art
Medical image data are normally acquired by medical imaging apparatuses and can represent anatomical structures and/or functional processes of the body of an examination subject. Medical image data of a single examination subject often are composed of medical image datasets that have been recorded at different points in time, for example. A typical problem to be addressed in this regard is how to compare the multiple medical image datasets with one another and identify variations between the multiple medical image datasets. For example, a characteristic of a change in the body of the examination subject as a function of time can be determined in a dynamic measurement.
In a perfusion imaging procedure, for example, there are typically a number of chronologically sequential medical image datasets available that describe a change in the content of a contrast agent in a tissue of the examination subject. A metric for a blood flow through the tissue can be derived therefrom.
In functional magnetic resonance imaging, signal variations between a number of medical image datasets typically indicate changes in the local oxygen uptake rate. From this it is possible to derive a metric for a change in the activity of functional centers in the brain of the examination subject, particularly in relation to a reaction to specific stimuli, such as optical stimuli.
Dynamic nuclear medical measurements, such as dynamic positron emission tomography (PET) measurements, are also known. In this case it is possible to measure the distribution of certain tracers, such as fluorodeoxyglucose, in the body of the examination subject over time.
Other medical imaging methods in which a number of medical image datasets of an examination subject are acquired, and are to be compared, are known to those skilled in the art.
Typically, it is difficult to detect minor variations between the number of medical image datasets in a qualitative sense. Dynamic measurements in particular are often limited in their signal-to-noise ratio due to their temporal resolution, with the result that variations in the medical image datasets are frequently hidden in the image noise.